Antarctica - ASV analysis
Antarctica - ASV analysis
- Initialize
- Get the Antarctica ASVs
- Get the different fraction separately
- Surface samples
- NMDS
- Vertical profile
- Compare the contributions of CLASS and SPECIS using the different methods
- Sample by sample comparison
- Define upset function
- Global comparison of species found with the 3 methods
- Comparison of species found in the different fractions for 18S filter and 18S sorted
- Comparison of species found in the different fractions for 18S filter
- Comparison of species found in the different fractions for 18S sort
- Figures
- CARBOM code
Initialize
Libraries
if(any(grepl("package:dvutils", search()))) detach("package:dvutils", unload=TRUE)
library(dvutils)
library(stringr)
library(ggplot2)
library(dplyr)
library(tidyr)
library(tibble)
library(readr)
library(maps)
library(glue)
library(readxl)
library(rlang)
library(phyloseq)
library(knitr)
library(rmdformats)
# library(pr2database)
# data("pr2")Markdown
Get the Antarctica ASVs
| set id | description |
|---|---|
| 16 | Antar_2015_18S_V4 |
| 17 | Antar_2015_16S_plastid |
| 18 | Antar_2015_18S_V4_sorted |
- Only use asv for which supergroup_boot >= 90
- Only keep photosynthetic groups abd exclude dinoflagellates
# rm(list = ls())
sample_type <- c("18S filter", "16S plastid", "18S sort")
taxon_selected <- c("Chlorophyta", "Cryptophyta", "Rhodophyta", "Haptophyta",
"Ochrophyta")
asv <- list()
ps <- list()
long <- list()
# Export as specific data set as a phyloseq file
i <- 16
for (one_sample_type in sample_type) {
asv[[one_sample_type]] <- metapr2_export_asv(dataset_id_selected = i, taxo_level = division,
taxo_name = taxon_selected, export_phyloseq = TRUE, export_long_xls = FALSE,
export_wide_xls = FALSE, export_fasta = FALSE, export_sample_xls = FALSE,
boot_min = 90, boot_level = supergroup_boot, directory = "../dada2/")
ps[[one_sample_type]] <- asv[[one_sample_type]][["ps"]]
# Label samples with date
sample_data(ps[[one_sample_type]])$sample_label <- as.character(sample_data(ps[[one_sample_type]])$date)
# Add TFF to sample name sample_data(ps[[one_sample_type]])$sample_label <-
# str_c(sample_data(ps[[one_sample_type]])$metadata_code,
# str_replace_na(sample_data(ps[[one_sample_type]])$sample_concentration,
# ''), sep = '.' )
print(glue("Phyloseq - {one_sample_type}"))
print(ps[[one_sample_type]])
cat("============================\n")
i <- i + 1
}Phyloseq - 18S filter
phyloseq-class experiment-level object
otu_table() OTU Table: [ 707 taxa and 123 samples ]
sample_data() Sample Data: [ 123 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 707 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 16S plastid
phyloseq-class experiment-level object
otu_table() OTU Table: [ 419 taxa and 103 samples ]
sample_data() Sample Data: [ 103 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 419 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S sort
phyloseq-class experiment-level object
otu_table() OTU Table: [ 264 taxa and 60 samples ]
sample_data() Sample Data: [ 60 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 264 taxa by 8 taxonomic ranks ]
============================
[1] "sample_id" "file_name"
[3] "sample_name" "sample_code"
[5] "metadata_code" "replicate"
[7] "DNA_RNA" "fraction_name"
[9] "fraction_min" "fraction_max"
[11] "sample_concentration" "sample_sorted"
[13] "reads_total" "sample_remark"
[15] "metadata_id" "metadata_code_original"
[17] "project" "cruise"
[19] "station_id" "station_id_num"
[21] "year" "date"
[23] "time" "season"
[25] "depth_level" "depth"
[27] "substrate" "substrate_description"
[29] "substrate_description_detailed" "experiment_name"
[31] "experiment_time" "experiment_time_unit"
[33] "experiment_bottle" "experiment_condition"
[35] "latitude" "longitude"
[37] "site_name" "country"
[39] "oceanic_region" "bottom_depth"
[41] "temperature" "salinity"
[43] "pH" "O2"
[45] "fluorescence" "ice_coverage"
[47] "Chla" "NO2"
[49] "NO3" "PO4"
[51] "Si" "Chla_0.2_3 um"
[53] "bact_ml" "syn_ml"
[55] "peuk_ml" "neuk_ml"
[57] "crypto_ml" "virus_small_ml"
[59] "virus_large_ml" "metadata_remark"
[61] "sample_label"
Get the different fraction separately
fraction_filter <- c(0.2, 3, 20)
fraction_sort <- c("pico", "nano")
sample_type_filtered <- c(str_c(sample_type[1], fraction_filter, "um", sep = " "),
str_c(sample_type[2], fraction_filter, "um", sep = " "), str_c(sample_type[3],
fraction_sort, sep = " "))
# Export as specific data set as a phyloseq file
for (one_sample_type in sample_type[1:2]) {
for (one_fraction in fraction_filter) {
one_sample_type_filtered <- c(str_c(one_sample_type, one_fraction, "um",
sep = " "))
ps[[one_sample_type_filtered]] <- ps[[one_sample_type]] %>% subset_samples(fraction_min ==
one_fraction) %>% filter_taxa(function(x) sum(x) > 0, TRUE)
print(glue("Phyloseq - {one_sample_type_filtered}"))
print(ps[[one_sample_type_filtered]])
cat("============================\n")
}
}Phyloseq - 18S filter 0.2 um
phyloseq-class experiment-level object
otu_table() OTU Table: [ 331 taxa and 43 samples ]
sample_data() Sample Data: [ 43 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 331 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S filter 3 um
phyloseq-class experiment-level object
otu_table() OTU Table: [ 377 taxa and 44 samples ]
sample_data() Sample Data: [ 44 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 377 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S filter 20 um
phyloseq-class experiment-level object
otu_table() OTU Table: [ 310 taxa and 36 samples ]
sample_data() Sample Data: [ 36 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 310 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 16S plastid 0.2 um
phyloseq-class experiment-level object
otu_table() OTU Table: [ 183 taxa and 29 samples ]
sample_data() Sample Data: [ 29 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 183 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 16S plastid 3 um
phyloseq-class experiment-level object
otu_table() OTU Table: [ 219 taxa and 42 samples ]
sample_data() Sample Data: [ 42 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 219 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 16S plastid 20 um
phyloseq-class experiment-level object
otu_table() OTU Table: [ 212 taxa and 32 samples ]
sample_data() Sample Data: [ 32 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 212 taxa by 8 taxonomic ranks ]
============================
# Sorted samples
for (one_sample_type in sample_type[3]) {
for (one_fraction in fraction_sort) {
one_sample_type_filtered <- c(str_c(one_sample_type, one_fraction, sep = " "))
ps[[one_sample_type_filtered]] <- ps[[one_sample_type]] %>% subset_samples(fraction_name ==
one_fraction) %>% filter_taxa(function(x) sum(x) > 0, TRUE)
print(glue("Phyloseq - {one_sample_type_filtered}"))
print(ps[[one_sample_type_filtered]])
cat("============================\n")
}
}Phyloseq - 18S sort pico
phyloseq-class experiment-level object
otu_table() OTU Table: [ 185 taxa and 30 samples ]
sample_data() Sample Data: [ 30 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 185 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S sort nano
phyloseq-class experiment-level object
otu_table() OTU Table: [ 132 taxa and 30 samples ]
sample_data() Sample Data: [ 30 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 132 taxa by 8 taxonomic ranks ]
============================
Surface samples
- Only use Station 6 (not 14)
- Only surface considered (5 m)
- Do not consider TFF samples
- Very important, must remove taxa that are not present in the filtered samples
sample_type_surface <- str_c(sample_type_all, " surface")
for (one_sample_type in sample_type_all) {
one_sample_type_surface = str_c(one_sample_type, " surface")
ps[[one_sample_type_surface]] <- ps[[one_sample_type]] %>% subset_samples(station_id ==
"6") %>% subset_samples(depth_level == "surface") %>% subset_samples(is.na(sample_concentration)) %>%
filter_taxa(function(x) sum(x) > 0, TRUE)
# Rename the samples
if (!str_detect(one_sample_type_surface, "sort")) {
sample_names(ps[[one_sample_type_surface]]) <- str_c(as.character(sample_data(ps[[one_sample_type_surface]])$date),
as.character(sample_data(ps[[one_sample_type_surface]])$depth),
"m", as.character(sample_data(ps[[one_sample_type_surface]])$fraction_min),
sep = "_")
} else {
sample_names(ps[[one_sample_type_surface]]) <- str_c(as.character(sample_data(ps[[one_sample_type_surface]])$date),
as.character(sample_data(ps[[one_sample_type_surface]])$depth),
"m", as.character(sample_data(ps[[one_sample_type_surface]])$fraction_name),
sep = "_")
}
print(glue("Phyloseq - {one_sample_type_surface}"))
print(ps[[one_sample_type_surface]])
cat("============================\n")
}Phyloseq - 18S filter surface
phyloseq-class experiment-level object
otu_table() OTU Table: [ 394 taxa and 50 samples ]
sample_data() Sample Data: [ 50 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 394 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 16S plastid surface
phyloseq-class experiment-level object
otu_table() OTU Table: [ 236 taxa and 40 samples ]
sample_data() Sample Data: [ 40 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 236 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S sort surface
phyloseq-class experiment-level object
otu_table() OTU Table: [ 114 taxa and 16 samples ]
sample_data() Sample Data: [ 16 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 114 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S filter 0.2 um surface
phyloseq-class experiment-level object
otu_table() OTU Table: [ 196 taxa and 17 samples ]
sample_data() Sample Data: [ 17 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 196 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S filter 3 um surface
phyloseq-class experiment-level object
otu_table() OTU Table: [ 229 taxa and 18 samples ]
sample_data() Sample Data: [ 18 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 229 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S filter 20 um surface
phyloseq-class experiment-level object
otu_table() OTU Table: [ 201 taxa and 15 samples ]
sample_data() Sample Data: [ 15 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 201 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 16S plastid 0.2 um surface
phyloseq-class experiment-level object
otu_table() OTU Table: [ 112 taxa and 11 samples ]
sample_data() Sample Data: [ 11 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 112 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 16S plastid 3 um surface
phyloseq-class experiment-level object
otu_table() OTU Table: [ 142 taxa and 16 samples ]
sample_data() Sample Data: [ 16 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 142 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 16S plastid 20 um surface
phyloseq-class experiment-level object
otu_table() OTU Table: [ 120 taxa and 13 samples ]
sample_data() Sample Data: [ 13 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 120 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S sort pico surface
phyloseq-class experiment-level object
otu_table() OTU Table: [ 65 taxa and 8 samples ]
sample_data() Sample Data: [ 8 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 65 taxa by 8 taxonomic ranks ]
============================
Phyloseq - 18S sort nano surface
phyloseq-class experiment-level object
otu_table() OTU Table: [ 70 taxa and 8 samples ]
sample_data() Sample Data: [ 8 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 70 taxa by 8 taxonomic ranks ]
============================
Normalize and transform to long form
for (one_sample_type in sample_type_surface) {
cat(one_sample_type)
cat("\n============================")
ps[[one_sample_type]] <- phyloseq_normalize_median(ps[[one_sample_type]])
long[[one_sample_type]] <- phyloseq_transform_to_long(ps[[one_sample_type]])
cat("\n")
}18S filter surface
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 394 taxa and 50 samples ]
sample_data() Sample Data: [ 50 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 394 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 23202
16S plastid surface
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 236 taxa and 40 samples ]
sample_data() Sample Data: [ 40 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 236 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 28885
18S sort surface
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 114 taxa and 16 samples ]
sample_data() Sample Data: [ 16 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 114 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 27275
18S filter 0.2 um surface
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 196 taxa and 17 samples ]
sample_data() Sample Data: [ 17 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 196 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 14647
18S filter 3 um surface
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 229 taxa and 18 samples ]
sample_data() Sample Data: [ 18 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 229 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 24783
18S filter 20 um surface
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 201 taxa and 15 samples ]
sample_data() Sample Data: [ 15 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 201 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 29427
16S plastid 0.2 um surface
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 112 taxa and 11 samples ]
sample_data() Sample Data: [ 11 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 112 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 28471
16S plastid 3 um surface
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 142 taxa and 16 samples ]
sample_data() Sample Data: [ 16 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 142 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 31801
16S plastid 20 um surface
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 120 taxa and 13 samples ]
sample_data() Sample Data: [ 13 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 120 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 25641
18S sort pico surface
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 65 taxa and 8 samples ]
sample_data() Sample Data: [ 8 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 65 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 33258
18S sort nano surface
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 70 taxa and 8 samples ]
sample_data() Sample Data: [ 8 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 70 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 21650
List of classes
classes <- data.frame(division = NULL, class = NULL)
for (one_sample_type in sample_type_surface[1:3]) {
classes <- bind_rows(classes, select(long[[one_sample_type]], division,
class))
}
classes <- classes %>% distinct(division, class) %>% arrange(division, class)
# print(classes, n=50)
knitr::kable(classes)| division | class |
|---|---|
| Chlorophyta | Chlorophyceae |
| Chlorophyta | Mamiellophyceae |
| Chlorophyta | Palmophyllophyceae |
| Chlorophyta | Prasino-Clade-V |
| Chlorophyta | Pyramimonadophyceae |
| Chlorophyta | Trebouxiophyceae |
| Chlorophyta | Ulvophyceae |
| Cryptophyta | Cryptophyceae |
| Haptophyta | Prymnesiophyceae |
| Ochrophyta | Bacillariophyta |
| Ochrophyta | Bolidophyceae |
| Ochrophyta | Chrysophyceae |
| Ochrophyta | Dictyochophyceae |
| Ochrophyta | MOCH-1 |
| Ochrophyta | MOCH-2 |
| Ochrophyta | Pelagophyceae |
| Ochrophyta | Phaeophyceae |
| Ochrophyta | Xanthophyceae |
| Rhodophyta | Bangiophyceae |
| Rhodophyta | Florideophyceae |
Treemaps
treemap_class <- list()
treemap_genus <- list()
for (one_sample_type in sample_type_surface) {
treemap_class[[one_sample_type]] <- phyloseq_long_treemap(long[[one_sample_type]],
division, class, str_c(one_sample_type, " - Class"), colors = class_colors)
treemap_genus[[one_sample_type]] <- phyloseq_long_treemap(long[[one_sample_type]],
class, genus, str_c(one_sample_type, " - Genus"))
}ASVs bargraphs
bargraph_asv <- list()
bargraph_species <- list()
for (one_sample_type in sample_type_surface) {
bargraph_asv[[one_sample_type]] <- phyloseq_long_bargraph(long[[one_sample_type]],
text_scaling = 0.75, n_bars = 20, use_asv = TRUE, title = one_sample_type)
bargraph_species[[one_sample_type]] <- phyloseq_long_bargraph(long[[one_sample_type]],
text_scaling = 0.75, n_bars = 20, use_asv = FALSE, title = one_sample_type)
} ## Barplots per sample
bargraph_sample <- list()
for (one_sample_type in sample_type_surface[4:11]) {
bargraph_sample[[one_sample_type]] <- plot_bar(ps[[one_sample_type]], x = "sample_label",
fill = "class") + geom_bar(aes(color = class, fill = class), stat = "identity",
position = "stack") + ggtitle(str_c("Class level - ", one_sample_type)) +
theme(axis.text.y = element_text(size = 10)) + theme(axis.text.x = element_text(angle = 0,
hjust = 0.5)) + coord_flip() + scale_fill_viridis_d() + scale_color_viridis_d()
print(bargraph_sample[[one_sample_type]])
}Heatmaps
Filter : Dictyo / Chrysophyceae /Pelago /Bacili / Crypto / Pyrami / Mamiello
Class
Most abundant 10%
heatmap_class_abundant <- list()
for (one_sample_type in sample_type_surface[4:11]) {
ps_heat <- tax_glom(ps[[one_sample_type]], taxrank = "class") %>% phyloseq_filter_abundant_taxa(fraction_min = 0.1)
heatmap_class_abundant[[one_sample_type]] <- plot_heatmap(ps_heat, method = "NMDS",
distance = "bray", taxa.label = "class", taxa.order = "division", sample.label = "sample_label",
sample.order = "sample_label", low = "beige", high = "red", na.value = "beige",
trans = scales::log_trans(10), title = one_sample_type) + xlab("") +
ylab("")
# plot(heatmap(otu_table(ps_heat)))
print(heatmap_class_abundant[[one_sample_type]])
}
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 7 taxa and 17 samples ]
sample_data() Sample Data: [ 17 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 7 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 13808
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 7 taxa and 18 samples ]
sample_data() Sample Data: [ 18 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 7 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 23366
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 5 taxa and 15 samples ]
sample_data() Sample Data: [ 15 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 5 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 27860
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 4 taxa and 11 samples ]
sample_data() Sample Data: [ 11 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 4 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 27592
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 5 taxa and 16 samples ]
sample_data() Sample Data: [ 16 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 5 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 31460
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 4 taxa and 13 samples ]
sample_data() Sample Data: [ 13 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 4 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 24881
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 4 taxa and 8 samples ]
sample_data() Sample Data: [ 8 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 4 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 32016
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 4 taxa and 8 samples ]
sample_data() Sample Data: [ 8 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 4 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 21054
Selected
class_selected <- c("Dictyochophyceae", "Chrysophyceae", "Pelagophyceae", "Bacillariophyta",
"Cryptophyceae", "Pyramimonadales", "Mamiellophyceae")
heatmap_class_selected <- list()
for (one_sample_type in sample_type_surface[4:11]) {
reads_max <- case_when(str_detect(one_sample_type, "18S filter") ~ 30000,
str_detect(one_sample_type, "16S plastid") ~ 30000, str_detect(one_sample_type,
"18S sort") ~ 25000)
ps_heat <- tax_glom(ps[[one_sample_type]], taxrank = "class") %>% subset_taxa(class %in%
class_selected)
# Try to order by division and class... tax_table <-
# data.frame(tax_table(ps_heat)@.Data) taxa_names(ps_heat) <-
# str_c(tax_table$division, tax_table$class, sep=' - ')
gg <- plot_heatmap(ps_heat, method = "NMDS", distance = "bray", taxa.label = "class",
taxa.order = "class", sample.label = "sample_label", sample.order = "sample_label",
low = "beige", high = "red", na.value = "gray95", trans = NULL, title = one_sample_type) +
xlab("") + ylab("") + theme(axis.text.x = element_text(angle = 45, hjust = 1,
vjust = 1)) + scale_fill_gradient(limits = c(0, reads_max), low = "beige",
high = "red", na.value = "gray95")
if (str_detect(one_sample_type, "18S filter 0.2"))
gg <- gg + geom_vline(xintercept = c(6.5, 9.5, 12.5))
if (one_sample_type == "18S filter 3 um surface")
gg <- gg + geom_vline(xintercept = c(8.5, 10.5, 13.5))
if (one_sample_type == "18S filter 20 um surface")
gg <- gg + geom_vline(xintercept = c(8.5, 9.5))
# plot(heatmap(otu_table(ps_heat))) , trans = scales::log_trans(10)
print(gg)
heatmap_class_selected[[one_sample_type]] <- gg
}Species
Most abundant
heatmap_genus_abundant <- list()
for (one_sample_type in sample_type_surface[4:11]) {
ps_heat <- tax_glom(ps[[one_sample_type]], taxrank = "species") %>% phyloseq_filter_abundant_taxa(fraction_min = 0.1)
heatmap_genus_abundant[[one_sample_type]] <- plot_heatmap(ps_heat, method = "NMDS",
distance = "bray", taxa.label = "species", taxa.order = "division",
sample.label = "sample_label", sample.order = "sample_label", low = "beige",
high = "red", na.value = "beige", title = one_sample_type)
print(heatmap_genus_abundant[[one_sample_type]])
}
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 11 taxa and 17 samples ]
sample_data() Sample Data: [ 17 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 11 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 11704
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 8 taxa and 18 samples ]
sample_data() Sample Data: [ 18 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 8 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 20196
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 11 taxa and 15 samples ]
sample_data() Sample Data: [ 15 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 11 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 26302
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 5 taxa and 11 samples ]
sample_data() Sample Data: [ 11 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 5 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 27286
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 8 taxa and 16 samples ]
sample_data() Sample Data: [ 16 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 8 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 30697
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 6 taxa and 13 samples ]
sample_data() Sample Data: [ 13 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 6 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 24093
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 5 taxa and 8 samples ]
sample_data() Sample Data: [ 8 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 5 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 28373
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 6 taxa and 8 samples ]
sample_data() Sample Data: [ 8 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 6 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 19513
Selected
species_selected <- c("Thalassiosira_minima", "Fragilariopsis_cylindrus", "Minidiscus_sp.",
"Chaetoceros_neogracilis", "Porosira_glacialis", "Coretrhon_inerme", "Palmaria_palmata",
"Pseudo-nitzchia_seriata", "Micromonas_polaris", "Micromonas_clade_B3",
"Bathycoccus_prasinos", "Pyramimonas_gelidicola", "Geminigera_cryophila",
"Pelagophyceae_XXX_sp.", "Phaeocystis_antarctica")
heatmap_species_selected <- list()
for (one_sample_type in sample_type_surface[4:11]) {
reads_max <- case_when(str_detect(one_sample_type, "18S filter") ~ 20000,
str_detect(one_sample_type, "16S plastid") ~ 5000, str_detect(one_sample_type,
"18S sort") ~ 15000)
ps_heat <- tax_glom(ps[[one_sample_type]], taxrank = "species") %>% subset_taxa(species %in%
species_selected)
# Try to order by division and species... tax_table <-
# data.frame(tax_table(ps_heat)@.Data) taxa_names(ps_heat) <-
# str_c(tax_table$division, tax_table$species, sep=' - ')
gg <- plot_heatmap(ps_heat, method = "NMDS", distance = "bray", taxa.label = "species",
taxa.order = "class", sample.label = "sample_label", sample.order = "sample_label",
low = "beige", high = "red", na.value = "gray95", trans = NULL, title = one_sample_type) +
xlab("") + ylab("") + theme(axis.text.x = element_text(angle = 45, hjust = 1,
vjust = 1)) + scale_fill_gradient(limits = c(0, reads_max), low = "beige",
high = "red", na.value = "gray95")
if (str_detect(one_sample_type, "18S filter 0.2"))
gg <- gg + geom_vline(xintercept = c(6.5, 9.5, 12.5))
if (one_sample_type == "18S filter 3 um surface")
gg <- gg + geom_vline(xintercept = c(8.5, 10.5, 13.5))
if (one_sample_type == "18S filter 20 um surface")
gg <- gg + geom_vline(xintercept = c(8.5, 9.5))
# plot(heatmap(otu_table(ps_heat))) , trans = scales::log_trans(10)
print(gg)
heatmap_species_selected[[one_sample_type]] <- gg
}NMDS
Apply to all samples
nmds_sample <- list()
for (one_sample_type in sample_type_surface[1:3]) {
one_ps <- ps[[one_sample_type]] %>% tax_glom(taxrank = "species")
nmds_sample[[one_sample_type]] <- phyloseq_nmds(one_ps, title = one_sample_type,
sample_color = season, sample_shape = fraction_name)
}Square root transformation
Wisconsin double standardization
Run 0 stress 0.1687566
Run 1 stress 0.1687634
... Procrustes: rmse 0.002985303 max resid 0.01690689
Run 2 stress 0.1687197
... New best solution
... Procrustes: rmse 0.002431247 max resid 0.01134347
Run 3 stress 0.1733998
Run 4 stress 0.1739068
Run 5 stress 0.1766939
Run 6 stress 0.1687159
... New best solution
... Procrustes: rmse 0.001111716 max resid 0.005731252
... Similar to previous best
Run 7 stress 0.1733984
Run 8 stress 0.1739071
Run 9 stress 0.1746154
Run 10 stress 0.1835842
Run 11 stress 0.168719
... Procrustes: rmse 0.001095126 max resid 0.005701493
... Similar to previous best
Run 12 stress 0.1835529
Run 13 stress 0.168719
... Procrustes: rmse 0.001110988 max resid 0.005739634
... Similar to previous best
Run 14 stress 0.168719
... Procrustes: rmse 0.001112726 max resid 0.005733226
... Similar to previous best
Run 15 stress 0.1733994
Run 16 stress 0.1745761
Run 17 stress 0.1721531
Run 18 stress 0.1734118
Run 19 stress 0.1872138
Run 20 stress 0.1687632
... Procrustes: rmse 0.003837324 max resid 0.01748068
*** Solution reached
[1] 3
Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance)
global Multidimensional Scaling using monoMDS
Data: wisconsin(sqrt(veganifyOTU(physeq)))
Distance: bray
Dimensions: 2
Stress: 0.1687159
Stress type 1, weak ties
Two convergent solutions found after 20 tries
Scaling: centring, PC rotation, halfchange scaling
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))'
Square root transformation
Wisconsin double standardization
Run 0 stress 0.1492484
Run 1 stress 0.1492468
... New best solution
... Procrustes: rmse 0.0002507616 max resid 0.001358323
... Similar to previous best
Run 2 stress 0.1489429
... New best solution
... Procrustes: rmse 0.01404688 max resid 0.06477591
Run 3 stress 0.1732926
Run 4 stress 0.1489447
... Procrustes: rmse 0.0005970766 max resid 0.003273668
... Similar to previous best
Run 5 stress 0.1526353
Run 6 stress 0.1489494
... Procrustes: rmse 0.001870841 max resid 0.01026137
Run 7 stress 0.1489076
... New best solution
... Procrustes: rmse 0.005462155 max resid 0.02114027
Run 8 stress 0.1489094
... Procrustes: rmse 0.0002241435 max resid 0.00122347
... Similar to previous best
Run 9 stress 0.1931313
Run 10 stress 0.1526371
Run 11 stress 0.1669089
Run 12 stress 0.2018991
Run 13 stress 0.188055
Run 14 stress 0.1526361
Run 15 stress 0.1547389
Run 16 stress 0.1760578
Run 17 stress 0.1762028
Run 18 stress 0.1965688
Run 19 stress 0.1755688
Run 20 stress 0.1870225
*** Solution reached
[1] 3
Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance)
global Multidimensional Scaling using monoMDS
Data: wisconsin(sqrt(veganifyOTU(physeq)))
Distance: bray
Dimensions: 2
Stress: 0.1489076
Stress type 1, weak ties
Two convergent solutions found after 20 tries
Scaling: centring, PC rotation, halfchange scaling
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))'
Square root transformation
Wisconsin double standardization
Run 0 stress 0.05090316
Run 1 stress 0.05736207
Run 2 stress 0.05090323
... Procrustes: rmse 0.0001234125 max resid 0.0002263345
... Similar to previous best
Run 3 stress 0.05185334
Run 4 stress 0.05090317
... Procrustes: rmse 3.166825e-05 max resid 5.733514e-05
... Similar to previous best
Run 5 stress 0.05090316
... Procrustes: rmse 3.131882e-05 max resid 5.132691e-05
... Similar to previous best
Run 6 stress 0.05161249
Run 7 stress 0.0520288
Run 8 stress 0.05379784
Run 9 stress 0.05723174
Run 10 stress 0.05161249
Run 11 stress 0.05379785
Run 12 stress 0.05220006
Run 13 stress 0.05940653
Run 14 stress 0.05533946
Run 15 stress 0.05344022
Run 16 stress 0.05220002
Run 17 stress 0.05161251
Run 18 stress 0.05379788
Run 19 stress 0.05161248
Run 20 stress 0.0520288
*** Solution reached
[1] 3
Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance)
global Multidimensional Scaling using monoMDS
Data: wisconsin(sqrt(veganifyOTU(physeq)))
Distance: bray
Dimensions: 2
Stress: 0.05090316
Stress type 1, weak ties
Two convergent solutions found after 20 tries
Scaling: centring, PC rotation, halfchange scaling
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))'
Vertical profile
- Station 6 - 2015-01-16
- Very important, must remove taxa that are not present in the filtered samples
- Do not use the TFF
Filter the data
sample_type_profile <- str_c(sample_type_all, " profile")
for (one_sample_type in sample_type_all) {
one_sample_type_profile = str_c(one_sample_type, " profile")
ps[[one_sample_type_profile]] <- ps[[one_sample_type]] %>% subset_samples(station_id ==
"6") %>% subset_samples(date == "2015-01-16") %>% subset_samples(is.na(sample_concentration)) %>%
filter_taxa(function(x) sum(x) > 0, TRUE) %>% phyloseq_normalize_median()
# Rename the samples
if (!str_detect(one_sample_type_profile, "sort")) {
sample_names(ps[[one_sample_type_profile]]) <- str_c(as.character(sample_data(ps[[one_sample_type_profile]])$depth),
"m", as.character(sample_data(ps[[one_sample_type_profile]])$fraction_min),
sep = "_")
} else {
sample_names(ps[[one_sample_type_profile]]) <- str_c(as.character(sample_data(ps[[one_sample_type_profile]])$depth),
"m", as.character(sample_data(ps[[one_sample_type_profile]])$fraction_name),
sep = "_")
}
print(glue("Phyloseq - {one_sample_type_profile}"))
print(ps[[one_sample_type_profile]])
cat("============================\n")
}
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 175 taxa and 15 samples ]
sample_data() Sample Data: [ 15 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 175 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 43491Phyloseq - 18S filter profile
phyloseq-class experiment-level object
otu_table() OTU Table: [ 175 taxa and 15 samples ]
sample_data() Sample Data: [ 15 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 175 taxa by 8 taxonomic ranks ]
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 107 taxa and 15 samples ]
sample_data() Sample Data: [ 15 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 107 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 41130Phyloseq - 16S plastid profile
phyloseq-class experiment-level object
otu_table() OTU Table: [ 107 taxa and 15 samples ]
sample_data() Sample Data: [ 15 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 107 taxa by 8 taxonomic ranks ]
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 96 taxa and 10 samples ]
sample_data() Sample Data: [ 10 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 96 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 29305Phyloseq - 18S sort profile
phyloseq-class experiment-level object
otu_table() OTU Table: [ 96 taxa and 10 samples ]
sample_data() Sample Data: [ 10 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 96 taxa by 8 taxonomic ranks ]
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 92 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 92 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 30265Phyloseq - 18S filter 0.2 um profile
phyloseq-class experiment-level object
otu_table() OTU Table: [ 92 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 92 taxa by 8 taxonomic ranks ]
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 86 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 86 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 31205Phyloseq - 18S filter 3 um profile
phyloseq-class experiment-level object
otu_table() OTU Table: [ 86 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 86 taxa by 8 taxonomic ranks ]
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 121 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 121 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 58797Phyloseq - 18S filter 20 um profile
phyloseq-class experiment-level object
otu_table() OTU Table: [ 121 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 121 taxa by 8 taxonomic ranks ]
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 46 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 46 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 40267Phyloseq - 16S plastid 0.2 um profile
phyloseq-class experiment-level object
otu_table() OTU Table: [ 46 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 46 taxa by 8 taxonomic ranks ]
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 62 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 62 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 46172Phyloseq - 16S plastid 3 um profile
phyloseq-class experiment-level object
otu_table() OTU Table: [ 62 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 62 taxa by 8 taxonomic ranks ]
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 74 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 74 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 47020Phyloseq - 16S plastid 20 um profile
phyloseq-class experiment-level object
otu_table() OTU Table: [ 74 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 74 taxa by 8 taxonomic ranks ]
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 65 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 65 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 29412Phyloseq - 18S sort pico profile
phyloseq-class experiment-level object
otu_table() OTU Table: [ 65 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 65 taxa by 8 taxonomic ranks ]
============================
==========
phyloseq-class experiment-level object
otu_table() OTU Table: [ 46 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 46 taxa by 8 taxonomic ranks ]
==========
The median number of reads used for normalization is 28502Phyloseq - 18S sort nano profile
phyloseq-class experiment-level object
otu_table() OTU Table: [ 46 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 61 sample variables ]
tax_table() Taxonomy Table: [ 46 taxa by 8 taxonomic ranks ]
============================
Barplots per depth
bargraph_sample <- list()
for (one_sample_type in sample_type_profile[4:11]) {
bargraph_sample[[one_sample_type]] <- plot_bar(ps[[one_sample_type]], x = "depth",
fill = "class") + geom_bar(aes(color = class, fill = class), stat = "identity",
position = "stack") + ggtitle(str_c("Class level - ", one_sample_type)) +
theme(axis.text.y = element_text(size = 10)) + theme(axis.text.x = element_text(angle = 0,
hjust = 0.5)) + coord_flip() + # scale_fill_viridis_d() + scale_color_viridis_d() +
scale_fill_manual(values = class_colors) + scale_color_manual(values = class_colors) +
scale_x_reverse() + xlab("Depth (m)") + ylab("Reads") + theme_bw()
print(bargraph_sample[[one_sample_type]])
}Compare the contributions of CLASS and SPECIS using the different methods
Sample by sample comparison
- Each dot corresponds to the relative contribution of the taxonomic level considered for a sample for which both 18S and 16S has been performed
long_species_sample <- list()
long_class_sample <- list()
for (one_sample_type in sample_type) {
ps_one <- phyloseq_normalize_percent(ps[[one_sample_type]])
long_one <- phyloseq_transform_to_long(ps_one) %>% group_by(date, station_id,
depth, fraction_name, division, class, order, family, genus, species) %>%
summarize(n_reads = sum(n_reads)) %>% ungroup()
long_species_sample[[one_sample_type]] <- long_one
long_one <- phyloseq_transform_to_long(ps_one) %>% group_by(date, station_id,
depth, fraction_name, division, class) %>% summarize(n_reads = sum(n_reads)) %>%
ungroup()
long_class_sample[[one_sample_type]] <- long_one
}
long_compare_species_18S_16S <- full_join(rename(long_species_sample[["18S filter"]],
n_reads_18S_filter = n_reads), rename(long_species_sample[["16S plastid"]],
n_reads_16S_plastid = n_reads))
ggplot(long_compare_species_18S_16S, aes(x = n_reads_18S_filter, y = n_reads_16S_plastid)) +
geom_point(aes(color = class)) + scale_color_manual(values = class_colors) +
ggtitle("Species")long_compare_class_18S_16S <- long_compare_species_18S_16S %>% group_by(date,
station_id, depth, fraction_name, division, class) %>% summarize(n_reads_18S_filter = sum(n_reads_18S_filter,
na.rm = TRUE), n_reads_16S_plastid = sum(n_reads_16S_plastid, na.rm = TRUE))
ggplot(long_compare_class_18S_16S, aes(x = n_reads_18S_filter, y = n_reads_16S_plastid)) +
geom_point(aes(color = class)) + scale_color_manual(values = class_colors) +
ggtitle("Classes")Define upset function
- Needs only to have 0 and 1 in the matrix
plot_upset_long <- function(long_comparison, file_name) {
long_upset <- long_comparison %>% mutate_if(is.numeric, funs(case_when(is.na(.) ~
0, TRUE ~ 1))) %>% as.data.frame()
fig_upset <- UpSetR::upset(long_upset, empty.intersections = "on", number.angles = 30,
point.size = 5, line.size = 2, mainbar.y.label = "Species intersections",
sets.x.label = "Species #", order.by = "freq", text.scale = c(2, 2,
2, 2, 2, 2), mb.ratio = c(0.7, 0.3))
# text.scale = c(intersection size title, intersection size tick labels, set
# size title, set size tick labels, set names, numbers above bars)
print(fig_upset)
png(file_name, width = 1000, height = 600, units = "px")
print(fig_upset)
dev.off()
}Global comparison of species found with the 3 methods
Compute table of number of samples for each species (rows) vs the three methods (columns)
Only keep
- 2015 samples because it is the only dataset for which we have the three types of samples
- 0.2 and 3 um fractions (to be comparable with sorting)
Remove species that contains _X
long_species <- list()
long_class <- list()
for (one_sample_type in sample_type) {
long_one <- long_species_sample[[one_sample_type]] %>% filter(date >= as.Date("2015-01-01")) %>%
filter(!str_detect(species, "_X")) %>% filter(fraction_name != "> 20 um") %>%
group_by(division, class, order, family, genus, species) %>% summarize(n_reads = sum(n_reads),
n_samples = n()) %>% rename_at(vars(contains("n_")), funs(str_c(., str_replace_all(one_sample_type,
" ", "_"), sep = "_"))) %>% ungroup()
long_species[[one_sample_type]] <- long_one
long_one <- long_class_sample[[one_sample_type]] %>% filter(date >= as.Date("2015-01-01")) %>%
filter(fraction_name != "> 20 um") %>% group_by(division, class) %>%
summarize(n_reads = sum(n_reads), n_samples = n()) %>% rename_at(vars(contains("n_")),
funs(str_c(., str_replace_all(one_sample_type, " ", "_"), sep = "_"))) %>%
ungroup()
long_class[[one_sample_type]] <- long_one
}
long_class_comparison <- long_class %>% purrr::reduce(full_join)
long_species_comparison <- long_species %>% purrr::reduce(full_join)
cat("Class not found in one type of sample\n")Class not found in one type of sample
18S filter
| division | class | n_reads_18S_filter | n_samples_18S_filter | n_reads_16S_plastid | n_samples_16S_plastid | n_reads_18S_sort | n_samples_18S_sort |
|---|---|---|---|---|---|---|---|
| Chlorophyta | Prasino-Clade-V | NA | NA | 0.404492 | 29 | NA | NA |
18S sort
| division | class | n_reads_18S_filter | n_samples_18S_filter | n_reads_16S_plastid | n_samples_16S_plastid | n_reads_18S_sort | n_samples_18S_sort |
|---|---|---|---|---|---|---|---|
| Chlorophyta | Trebouxiophyceae | 0.0004002 | 3 | 0.0004331 | 3 | NA | NA |
| Rhodophyta | Florideophyceae | 0.0755969 | 29 | 0.0018921 | 10 | NA | NA |
| Chlorophyta | Prasino-Clade-V | NA | NA | 0.4044920 | 29 | NA | NA |
16S filter
| division | class | n_reads_18S_filter | n_samples_18S_filter | n_reads_16S_plastid | n_samples_16S_plastid | n_reads_18S_sort | n_samples_18S_sort |
|---|---|---|---|---|---|---|---|
| Chlorophyta | Ulvophyceae | 0.0258306 | 24 | NA | NA | 0.5714412 | 4 |
| Ochrophyta | MOCH-2 | 0.0334232 | 28 | NA | NA | 0.0449041 | 11 |
Species found in the three types of samples
18S filter
kable(filter(long_species_comparison, (!is.na(n_samples_18S_filter) & !is.na(n_samples_18S_sort) &
!is.na(n_samples_16S_plastid))))| division | class | order | family | genus | species | n_reads_18S_filter | n_samples_18S_filter | n_reads_16S_plastid | n_samples_16S_plastid | n_reads_18S_sort | n_samples_18S_sort |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Chlorophyta | Mamiellophyceae | Mamiellales | Bathycoccaceae | Bathycoccus | Bathycoccus_prasinos | 0.2422204 | 32 | 0.0038980 | 3 | 1.1036274 | 23 |
| Chlorophyta | Mamiellophyceae | Mamiellales | Mamiellaceae | Mantoniella | Mantoniella_squamata | 0.0004829 | 1 | 0.0559522 | 24 | 0.0013399 | 1 |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Geminigera | Geminigera_cryophila | 8.7159220 | 35 | 0.0000594 | 1 | 5.9759821 | 30 |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_antarctica | 0.9069443 | 35 | 0.0297455 | 10 | 11.2070634 | 42 |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_sp. | 0.0004760 | 1 | 7.1265134 | 30 | 0.1486057 | 14 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_sp. | 1.4038636 | 34 | 4.1583544 | 30 | 0.8233117 | 16 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Fragilariopsis | Fragilariopsis_cylindrus | 4.2004487 | 35 | 1.1061643 | 30 | 6.6987788 | 42 |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Florenciellales | Florenciella | Florenciella_parvula | 0.0539616 | 30 | 0.0021032 | 4 | 0.0109493 | 9 |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Ectocarpus | Ectocarpus_siliculosus | 0.0193789 | 1 | 0.0004854 | 2 | 0.0031598 | 2 |
Species only found in one type of sample
18S filter
| division | class | order | family | genus | species | n_reads_18S_filter | n_samples_18S_filter | n_reads_16S_plastid | n_samples_16S_plastid | n_reads_18S_sort | n_samples_18S_sort |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Chlorophyta | Chlorophyceae | Chaetopeltidales | Chaetopeltidaceae | Planophila | Planophila_sp. | 0.0003221 | 1 | NA | NA | NA | NA |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Chlamydomonas | Chlamydomonas_acidophila | 0.0001343 | 1 | NA | NA | NA | NA |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Chlamydomonas | Chlamydomonas_hedleyi | 0.0006203 | 2 | NA | NA | NA | NA |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Chlamydomonas | Chlamydomonas_kuwadae | 0.0013583 | 6 | NA | NA | NA | NA |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Haematococcus | Haematococcus_zimbabwiensis | 0.0001602 | 1 | NA | NA | NA | NA |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Pleurastrum | Pleurastrum_sp. | 0.0009742 | 3 | NA | NA | NA | NA |
| Chlorophyta | Trebouxiophyceae | Prasiolales | Prasiolales_X | Desmococcus | Desmococcus_endolithicus | 0.0000585 | 1 | NA | NA | NA | NA |
| Chlorophyta | Trebouxiophyceae | Watanabea-Clade | Watanabea-Clade_X | Chloroidium | Chloroidium_ellipsoideum | 0.0001530 | 1 | NA | NA | NA | NA |
| Chlorophyta | Ulvophyceae | Ulotrichales | Ulotrichales_X | Chlorothrix | Chlorothrix_sp. | 0.0018758 | 2 | NA | NA | NA | NA |
| Chlorophyta | Ulvophyceae | Ulvales-relatives | Ulvales-relatives_X | Dilabifilum | Dilabifilum_sp. | 0.0002731 | 1 | NA | NA | NA | NA |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Falcomonas | Falcomonas_sp. | 0.0002160 | 2 | NA | NA | NA | NA |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Hemiselmis | Hemiselmis_tepida | 0.0061589 | 7 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Grammonema | Grammonema_striatula | 0.0003498 | 3 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Licmophora | Licmophora_grandis | 0.0007432 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Pteroncola | Pteroncola_inane | 0.0007406 | 4 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Thalassionema | Thalassionema_frauenfeldii | 0.0011473 | 2 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_peruvianus | 0.0003611 | 3 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Eucampia | Eucampia_antarctica | 0.0005297 | 3 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Hemiaulus | Hemiaulus_sinensis | 0.0001122 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Minidiscus | Minidiscus_trioculatus | 0.0354069 | 7 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Odontella | Odontella_aurita | 0.0000751 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Porosira | Porosira_pseudodelicatula | 0.0003758 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Shionodiscus | Shionodiscus_ritscheri | 0.0173100 | 2 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_antarctica | 0.0001088 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_minuscula | 0.0000748 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_tumida | 0.0007959 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Actinocyclus | Actinocyclus_actinochilus | 0.0017113 | 4 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Rhizosolenia | Rhizosolenia_fallax | 0.0000685 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Stellarima | Stellarima_microtrias | 0.0001769 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Amphora | Amphora_proteus | 0.0057413 | 2 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Cylindrotheca | Cylindrotheca_closterium | 0.0002630 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Cymbella | Cymbella_gastroides | 0.0041439 | 12 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Encyonema | Encyonema_sp. | 0.0016080 | 7 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Haslea | Haslea_spicula | 0.0011842 | 4 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Navicula | Navicula_perminuta | 0.0000805 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Navicula | Navicula_sp. | 0.0008106 | 3 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Nitzschia | Nitzschia_dissipata | 0.0001239 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Nitzschia | Nitzschia_sp. | 0.0001170 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pauliella | Pauliella_toeniata | 0.0002738 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pleurosigma | Pleurosigma_intermedium | 0.0011815 | 3 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pseudo-nitzschia | Pseudo-nitzschia_turgidula | 0.0001264 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pseudogomphonema | Pseudogomphonema_sp. | 0.0059704 | 11 | NA | NA | NA | NA |
| Ochrophyta | Bolidophyceae | Parmales | Parmales_env_3 | Parmales_env_3A | Parmales_env_3A_sp. | 0.0120071 | 10 | NA | NA | NA | NA |
| Ochrophyta | Chrysophyceae | Chrysophyceae_X | Chrysophyceae_Clade-C | Spumella | Spumella_elongata | 0.0144543 | 5 | NA | NA | NA | NA |
| Ochrophyta | Chrysophyceae | Chrysophyceae_X | Chrysophyceae_Clade-F | Paraphysomonas | Paraphysomonas_imperforata | 0.0026271 | 5 | NA | NA | NA | NA |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Pedinellales | Pseudopedinella | Pseudopedinella_sp. | 0.0001042 | 1 | NA | NA | NA | NA |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Pylaiella | Pylaiella_littoralis | 0.0005950 | 1 | NA | NA | NA | NA |
| Rhodophyta | Florideophyceae | Ceramiales | Delesseriaceae | Hemineura | Hemineura_frondosa | 0.0000743 | 1 | NA | NA | NA | NA |
| Rhodophyta | Florideophyceae | Ceramiales | Delesseriaceae | Phycodrys | Phycodrys_rubens | 0.0000850 | 1 | NA | NA | NA | NA |
| Rhodophyta | Florideophyceae | Corallinales | Corallinales_X | Hapalidiaceae | Hapalidiaceae_sp. | 0.0003964 | 1 | NA | NA | NA | NA |
| Rhodophyta | Florideophyceae | Corallinales | Corallinales_X | Mesophyllum | Mesophyllum_sp. | 0.0267372 | 24 | NA | NA | NA | NA |
| Rhodophyta | Florideophyceae | Corallinales | Corallinales_X | Synarthrophyton | Synarthrophyton_patena | 0.0007191 | 2 | NA | NA | NA | NA |
| Rhodophyta | Florideophyceae | Gigartinales | Gigartinales_X | Chondrus | Chondrus_nipponicus | 0.0420698 | 23 | NA | NA | NA | NA |
| Rhodophyta | Florideophyceae | Gigartinales | Gigartinales_X | Stenogramme | Stenogramme_interrupta | 0.0015735 | 2 | NA | NA | NA | NA |
| Rhodophyta | Florideophyceae | Gracilariales | Gracilariales_X | Curdiea | Curdiea_codiodes | 0.0011262 | 4 | NA | NA | NA | NA |
| Rhodophyta | Florideophyceae | Halymeniales | Halymeniales_X | Pachymenia | Pachymenia_orbicularis | 0.0000991 | 1 | NA | NA | NA | NA |
| Rhodophyta | Florideophyceae | Plocamiales | Plocamiales_X | Trematocarpus | Trematocarpus_fragilis | 0.0004336 | 2 | NA | NA | NA | NA |
18S sort
kable(filter(long_species_comparison, (is.na(n_samples_18S_filter) & is.na(n_samples_16S_plastid))))| division | class | order | family | genus | species | n_reads_18S_filter | n_samples_18S_filter | n_reads_16S_plastid | n_samples_16S_plastid | n_reads_18S_sort | n_samples_18S_sort |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Chlorophyta | Ulvophyceae | Ulotrichales | Ulotrichales_X | Ulothrix | Ulothrix_zonata | NA | NA | NA | NA | 0.0014895 | 1 |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Falcomonas | Falcomonas_daucoides | NA | NA | NA | NA | 0.0026517 | 4 |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Hemiselmis | Hemiselmis_sp. | NA | NA | NA | NA | 0.0001032 | 1 |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_pouchetii | NA | NA | NA | NA | 0.0021350 | 3 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Bacterosira | Bacterosira_sp. | NA | NA | NA | NA | 0.0000804 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Skeletonema | Skeletonema_sp. | NA | NA | NA | NA | 0.0000463 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Bacillaria | Bacillaria_paxillifer | NA | NA | NA | NA | 0.0002977 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Luka_AeN707 | Luka_AeN707-96_sp. | NA | NA | NA | NA | 0.0016083 | 3 |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Florenciellales | Pseudochattonella | Pseudochattonella_verruculosa | NA | NA | NA | NA | 0.0012825 | 1 |
| Ochrophyta | Pelagophyceae | Pelagomonadales | Pelagomonadaceae | Pelagomonadaceae_clade_A | Pelagomonadaceae_clade_A_sp. | NA | NA | NA | NA | 0.0010176 | 2 |
| Ochrophyta | Pelagophyceae | Sarcinochrysidales | Sarcinochrysidaceae | Ankylochrysis | Ankylochrysis_sp. | NA | NA | NA | NA | 0.0000885 | 1 |
| Ochrophyta | Pelagophyceae | Sarcinochrysidales | Sarcinochrysidaceae | Sarcinochrysis | Sarcinochrysis_marina | NA | NA | NA | NA | 0.0000491 | 1 |
16S filter
| division | class | order | family | genus | species | n_reads_18S_filter | n_samples_18S_filter | n_reads_16S_plastid | n_samples_16S_plastid | n_reads_18S_sort | n_samples_18S_sort |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Oophila | Oophila_amblystomatis | NA | NA | 0.0001469 | 2 | NA | NA |
| Chlorophyta | Palmophyllophyceae | Prasinococcales | Prasinococcales-Clade-B | Prasinoderma | Prasinoderma_coloniale | NA | NA | 0.0200905 | 24 | NA | NA |
| Chlorophyta | Pyramimonadophyceae | Pyramimonadales | Pyramimonadaceae | Pyramimonas | Pyramimonas_disomata | NA | NA | 0.0001915 | 2 | NA | NA |
| Chlorophyta | Pyramimonadophyceae | Pyramimonadales | Pyramimonadaceae | Pyramimonas | Pyramimonas_sp. | NA | NA | 4.9391166 | 30 | NA | NA |
| Chlorophyta | Trebouxiophyceae | Chlorellales | Chlorellales_X | Chlorella | Chlorella_vulgaris | NA | NA | 0.0001729 | 1 | NA | NA |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_globosa | NA | NA | 4.1611794 | 30 | NA | NA |
| Haptophyta | Prymnesiophyceae | Prymnesiales | Prymnesiaceae | Dicrateria | Dicrateria_sp. | NA | NA | 0.0064689 | 17 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Grammonema | Grammonema_striatulum | NA | NA | 0.0003316 | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_sp. | NA | NA | 0.1147767 | 26 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Conticribra | Conticribra_weissflogii | NA | NA | 0.0001386 | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Lauderia | Lauderia_annulata | NA | NA | 0.0000594 | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Odontella | Odontella_sinensis | NA | NA | 0.1707281 | 25 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_nordenskioeldii | NA | NA | 0.0001708 | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Corethron | Corethron_pennatum | NA | NA | 0.0290473 | 22 | NA | NA |
| Ochrophyta | Bolidophyceae | Parmales | Triparmaceae | Triparma | Triparma_pacifica | NA | NA | 0.0103977 | 5 | NA | NA |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Pedinellales | Helicopedinella | Helicopedinella_sp. | NA | NA | 0.0073091 | 5 | NA | NA |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Pedinellales | Mesopedinella | Mesopedinella_arctica | NA | NA | 0.0004326 | 3 | NA | NA |
| Ochrophyta | Pelagophyceae | Pelagomonadales | Pelagomonadaceae | Aureococcus | Aureococcus_anophagefferens | NA | NA | 0.4848461 | 30 | NA | NA |
| Rhodophyta | Florideophyceae | Ahnfeltiales | Ahnfeltiaceae | Ahnfeltia | Ahnfeltia_plicata | NA | NA | 0.0001100 | 1 | NA | NA |
| Rhodophyta | Florideophyceae | Gigartinales | Gigartinales_X | Chondrus | Chondrus_crispus | NA | NA | 0.0012824 | 8 | NA | NA |
openxlsx::write.xlsx(list(long_class_comparison, long_species_comparison), file = "../dada2/method_comparison.xlsx",
sheetName = c("Class", "Species"))
long_species_comparison_upset <- long_species_comparison %>% select(species,
contains("samples")) %>% rename_at(vars(contains("n_")), funs(str_replace(.,
"n_samples_", "")))
plot_upset_long(long_species_comparison_upset, "fig/upset_method.png")png
2
Comparison of species found in the different fractions for 18S filter and 18S sorted
Compute table of number of samples for each species (rows) vs the three fractions (columns)
Only keep
- 2015 samples because it is the only dataset for which we have the three types of samples
- Genera that do not contain _X
long_species_fraction <- list()
long_class_fraction <- list()
for (one_sample_type in sample_type[c(1, 3)]) {
long_one <- long_species_sample[[one_sample_type]] %>% filter(date >= as.Date("2015-01-01")) %>%
filter(!str_detect(species, "_X")) %>% group_by(fraction_name, division,
class, order, family, genus, species) %>% summarize(n_samples = n()) %>%
pivot_wider(names_from = fraction_name, values_from = n_samples) %>%
ungroup()
long_species_fraction[[one_sample_type]] <- long_one
long_one <- long_class_sample[[one_sample_type]] %>% filter(date >= as.Date("2015-01-01")) %>%
group_by(fraction_name, division, class) %>% summarize(n_samples = n()) %>%
pivot_wider(names_from = fraction_name, values_from = n_samples) %>%
ungroup()
long_class_fraction[[one_sample_type]] <- long_one
}
long_class_fraction_comparison <- long_class_fraction %>% purrr::reduce(full_join)
long_species_fraction_comparison <- long_species_fraction %>% purrr::reduce(full_join)
kable(long_class_fraction_comparison)| division | class | > 20 um | 0.2-3 um | 3-20 um | nano | pico |
|---|---|---|---|---|---|---|
| Chlorophyta | Chlorophyceae | 7 | 5 | 5 | 1 | NA |
| Chlorophyta | Mamiellophyceae | 17 | 18 | 17 | 18 | 22 |
| Chlorophyta | Palmophyllophyceae | 4 | 15 | 16 | 1 | 1 |
| Chlorophyta | Pyramimonadophyceae | 18 | 18 | 17 | 19 | 4 |
| Chlorophyta | Trebouxiophyceae | 4 | NA | 3 | NA | NA |
| Chlorophyta | Ulvophyceae | 14 | 11 | 13 | 2 | 2 |
| Cryptophyta | Cryptophyceae | 19 | 18 | 17 | 22 | 9 |
| Haptophyta | Prymnesiophyceae | 19 | 18 | 17 | 22 | 22 |
| Ochrophyta | Bacillariophyta | 19 | 18 | 17 | 22 | 22 |
| Ochrophyta | Bolidophyceae | 19 | 18 | 17 | 21 | 20 |
| Ochrophyta | Chrysophyceae | 14 | 17 | 17 | 2 | 2 |
| Ochrophyta | Dictyochophyceae | 11 | 18 | 17 | 15 | 1 |
| Ochrophyta | MOCH-2 | 10 | 13 | 15 | 11 | NA |
| Ochrophyta | Pelagophyceae | 19 | 18 | 17 | 22 | 22 |
| Ochrophyta | Phaeophyceae | 19 | 12 | 17 | 10 | NA |
| Rhodophyta | Florideophyceae | 18 | 13 | 16 | NA | NA |
| division | class | order | family | genus | species | > 20 um | 0.2-3 um | 3-20 um | nano | pico |
|---|---|---|---|---|---|---|---|---|---|---|
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Chlamydomonas | Chlamydomonas_kuwadae | 5 | 3 | 3 | NA | NA |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Chlamydomonas | Chlamydomonas_raudensis | 1 | NA | 1 | 1 | NA |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Pleurastrum | Pleurastrum_sp. | 1 | 1 | 2 | NA | NA |
| Chlorophyta | Chlorophyceae | Sphaeropleales | Sphaeropleales_X | Radiococcus | Radiococcus_polycoccus | 1 | NA | NA | NA | NA |
| Chlorophyta | Mamiellophyceae | Dolichomastigales | Dolichomastigaceae | Dolichomastigaceae-B | Dolichomastigaceae-B_sp. | 12 | 18 | 17 | 1 | 5 |
| Chlorophyta | Mamiellophyceae | Mamiellales | Bathycoccaceae | Bathycoccus | Bathycoccus_prasinos | 5 | 17 | 15 | 1 | 22 |
| Chlorophyta | Mamiellophyceae | Mamiellales | Mamiellaceae | Micromonas | Micromonas_polaris | 17 | 17 | 17 | 17 | 22 |
| Chlorophyta | Palmophyllophyceae | Prasinococcales | Prasinococcales-Clade-B | Prasinoderma | Prasinoderma_sp. | 4 | 15 | 16 | 1 | 1 |
| Chlorophyta | Pyramimonadophyceae | Pyramimonadales | Pyramimonadaceae | Pyramimonas | Pyramimonas_australis | 13 | 17 | 17 | 10 | 2 |
| Chlorophyta | Pyramimonadophyceae | Pyramimonadales | Pyramimonadaceae | Pyramimonas | Pyramimonas_gelidicola | 18 | 18 | 17 | 19 | 4 |
| Chlorophyta | Trebouxiophyceae | Chlorellales | Chlorellales_X | Chlorella | Chlorella_sorokiniana | 1 | NA | NA | NA | NA |
| Chlorophyta | Ulvophyceae | Ulotrichales | Ulotrichales_X | Chlorothrix | Chlorothrix_sp. | 4 | NA | 2 | NA | NA |
| Chlorophyta | Ulvophyceae | Ulotrichales | Ulotrichales_X | Monostroma | Monostroma_grevillei | 10 | 9 | 10 | 2 | 1 |
| Chlorophyta | Ulvophyceae | Ulotrichales | Ulotrichales_X | Ulothrix | Ulothrix_zonata | 2 | NA | NA | 1 | NA |
| Chlorophyta | Ulvophyceae | Ulvales-relatives | Ulvales-relatives_X | Acrochaete | Acrochaete_leptochaete | 2 | 3 | 6 | NA | 1 |
| Chlorophyta | Ulvophyceae | Ulvales-relatives | Ulvales-relatives_X | Dilabifilum | Dilabifilum_sp. | 1 | 1 | NA | NA | NA |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Geminigera | Geminigera_cryophila | 19 | 18 | 17 | 22 | 8 |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Hemiselmis | Hemiselmis_tepida | 2 | 4 | 3 | NA | NA |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_antarctica | 19 | 18 | 17 | 22 | 20 |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_sp. | 1 | NA | 1 | 12 | 2 |
| Haptophyta | Prymnesiophyceae | Prymnesiales | Chrysochromulinaceae | Chrysochromulina | Chrysochromulina_sp. | 3 | 9 | 9 | 22 | 5 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Asteroplanus | Asteroplanus_karianus | 19 | 18 | 17 | 20 | 3 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Grammonema | Grammonema_striatula | 2 | NA | 3 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Licmophora | Licmophora_grandis | 7 | 1 | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Pteroncola | Pteroncola_inane | 7 | 1 | 3 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Synedra | Synedra_hyperborea | 2 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Synedropsis | Synedropsis_recta | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Tabularia | Tabularia_tabulata | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Thalassiothrix | Thalassiothrix_longissima | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_debilis_2 | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_neogracilis | 19 | 18 | 17 | 16 | 19 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_peruvianus | 1 | 2 | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_rostratus | 2 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_socialis | 18 | 14 | 7 | 3 | 3 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Eucampia | Eucampia_antarctica | 2 | 2 | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Minidiscus | Minidiscus_sp. | 19 | 18 | 17 | 22 | 10 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Odontella | Odontella_aurita | 2 | NA | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Odontella | Odontella_mobiliensis | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Porosira | Porosira_glacialis | 19 | 18 | 17 | 2 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Porosira | Porosira_pseudodelicatula | 4 | 1 | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Porosira | Porosira_pseudodenticulata | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Shionodiscus | Shionodiscus_ritscheri | 12 | 2 | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_antarctica | 1 | NA | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_minima | 19 | 18 | 17 | 21 | 11 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_oceanica | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_sp. | 19 | 17 | 17 | 14 | 2 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_tumida | 1 | 1 | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Actinocyclus | Actinocyclus_actinochilus | 11 | 2 | 2 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Corethron | Corethron_inerme | 19 | 18 | 17 | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Proboscia | Proboscia_alata | 7 | 5 | 8 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Proboscia | Proboscia_inermis | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Rhizosolenia | Rhizosolenia_fallax | 1 | 1 | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Rhizosolenia | Rhizosolenia_imbricata_var_shrubsolei | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Stellarima | Stellarima_microtrias | 3 | 1 | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Achnanthes | Achnanthes_bongranii | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Amphora | Amphora_proteus | 12 | 2 | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Cocconeis | Cocconeis_stauroneiformis | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Cymbella | Cymbella_gastroides | 7 | 3 | 9 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Cymbella | Cymbella_laevis | 2 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Cymbella | Cymbella_salina | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Dickieia | Dickieia_ulvacea | 4 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Encyonema | Encyonema_sp. | 6 | 1 | 6 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Fragilariopsis | Fragilariopsis_cylindrus | 19 | 18 | 17 | 22 | 20 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Fragilariopsis | Fragilariopsis_sublineata | 11 | 4 | 17 | 6 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Gyrosigma | Gyrosigma_limosum | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Haslea | Haslea_spicula | 3 | 2 | 2 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Navicula | Navicula_phyllepta | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Navicula | Navicula_sp. | 4 | 2 | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Naviculales | Naviculales_sp. | 4 | 8 | 11 | 4 | 2 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pinnularia | Pinnularia_microstauron | 1 | NA | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pleurosigma | Pleurosigma_intermedium | 3 | 1 | 2 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pseudo-nitzschia | Pseudo-nitzschia_seriata | 15 | 10 | 16 | 6 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pseudogomphonema | Pseudogomphonema_sp. | 10 | 5 | 6 | NA | NA |
| Ochrophyta | Bolidophyceae | Parmales | Parmales_env_3 | Parmales_env_3A | Parmales_env_3A_sp. | 2 | 10 | NA | NA | NA |
| Ochrophyta | Bolidophyceae | Parmales | Parmales_env_3 | Parmales_env_3B | Parmales_env_3B_sp. | 17 | 18 | 17 | 21 | 15 |
| Ochrophyta | Bolidophyceae | Parmales | Triparmaceae | Triparma | Triparma_laevis_clade | 19 | 18 | 17 | 11 | 17 |
| Ochrophyta | Chrysophyceae | Chrysophyceae_X | Chrysophyceae_Clade-F | Paraphysomonas | Paraphysomonas_imperforata | 1 | 3 | 2 | NA | NA |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Dictyochales | Dictyocha | Dictyocha_speculum | 1 | NA | NA | NA | NA |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Florenciellales | Florenciella | Florenciella_parvula | 7 | 15 | 15 | 9 | NA |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Florenciellales | Pseudochattonella | Pseudochattonella_farcimen | 6 | 13 | 16 | 9 | NA |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Florenciellales | Pseudochattonella | Pseudochattonella_sp. | 1 | 1 | 5 | 2 | NA |
| Ochrophyta | Pelagophyceae | Pelagomonadales | Pelagomonadaceae | Pelagomonas | Pelagomonas_calceolata | 3 | 11 | 4 | 3 | 18 |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Desmarestia | Desmarestia_menziesii | 14 | 4 | 11 | 3 | NA |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Desmarestia | Desmarestia_sp. | 13 | 4 | 9 | 3 | NA |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Ectocarpus | Ectocarpus_siliculosus | 4 | NA | 1 | 2 | NA |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Phaeurus | Phaeurus_antarcticus | 16 | 9 | 15 | 7 | NA |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Pylaiella | Pylaiella_littoralis | 1 | NA | 1 | NA | NA |
| Rhodophyta | Florideophyceae | Ceramiales | Callithamniaceae | Diapse | Diapse_ptilota | 1 | NA | NA | NA | NA |
| Rhodophyta | Florideophyceae | Ceramiales | Delesseriaceae | Phycodrys | Phycodrys_rubens | 4 | NA | 1 | NA | NA |
| Rhodophyta | Florideophyceae | Ceramiales | Sarcomeniaceae | Platysiphonia | Platysiphonia_victoriae | 1 | NA | NA | NA | NA |
| Rhodophyta | Florideophyceae | Colaconematales | Colaconematales_X | Palmaria | Palmaria_palmata | 1 | 3 | 3 | NA | NA |
| Rhodophyta | Florideophyceae | Corallinales | Corallinales_X | Hapalidiaceae | Hapalidiaceae_sp. | 6 | 1 | NA | NA | NA |
| Rhodophyta | Florideophyceae | Corallinales | Corallinales_X | Mesophyllum | Mesophyllum_sp. | 17 | 10 | 14 | NA | NA |
| Rhodophyta | Florideophyceae | Corallinales | Corallinales_X | Synarthrophyton | Synarthrophyton_patena | 8 | 1 | 1 | NA | NA |
| Rhodophyta | Florideophyceae | Gigartinales | Gigartinales_X | Chondrus | Chondrus_nipponicus | 18 | 7 | 16 | NA | NA |
| Rhodophyta | Florideophyceae | Gigartinales | Gigartinales_X | Schottera | Schottera_nicaeensis | 1 | NA | NA | NA | NA |
| Rhodophyta | Florideophyceae | Gigartinales | Gigartinales_X | Stenogramme | Stenogramme_interrupta | 3 | NA | 2 | NA | NA |
| Rhodophyta | Florideophyceae | Gracilariales | Gracilariales_X | Curdiea | Curdiea_codiodes | 7 | 1 | 3 | NA | NA |
| Rhodophyta | Florideophyceae | Plocamiales | Plocamiales_X | Trematocarpus | Trematocarpus_fragilis | 7 | 1 | 1 | NA | NA |
| Chlorophyta | Chlorophyceae | Chaetopeltidales | Chaetopeltidaceae | Planophila | Planophila_sp. | NA | 1 | NA | NA | NA |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Chlamydomonas | Chlamydomonas_acidophila | NA | 1 | NA | NA | NA |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Chlamydomonas | Chlamydomonas_hedleyi | NA | 1 | 1 | NA | NA |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Haematococcus | Haematococcus_zimbabwiensis | NA | 1 | NA | NA | NA |
| Chlorophyta | Mamiellophyceae | Mamiellales | Mamiellaceae | Mantoniella | Mantoniella_squamata | NA | 1 | NA | 1 | NA |
| Chlorophyta | Mamiellophyceae | Mamiellales | Mamiellaceae | Micromonas | Micromonas_clade_B3 | NA | 8 | NA | NA | 21 |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Teleaulax | Teleaulax_sp. | NA | 1 | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Thalassionema | Thalassionema_frauenfeldii | NA | 1 | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Minidiscus | Minidiscus_trioculatus | NA | 2 | 5 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Nitzschia | Nitzschia_dissipata | NA | 1 | NA | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pauliella | Pauliella_toeniata | NA | 1 | NA | NA | NA |
| Ochrophyta | Chrysophyceae | Chrysophyceae_X | Chrysophyceae_Clade-C | Spumella | Spumella_elongata | NA | 5 | NA | NA | NA |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Pedinellales | Pseudopedinella | Pseudopedinella_sp. | NA | 1 | NA | NA | NA |
| Rhodophyta | Florideophyceae | Ceramiales | Delesseriaceae | Hemineura | Hemineura_frondosa | NA | 1 | NA | NA | NA |
| Rhodophyta | Florideophyceae | Halymeniales | Halymeniales_X | Pachymenia | Pachymenia_orbicularis | NA | 1 | NA | NA | NA |
| Chlorophyta | Trebouxiophyceae | Prasiolales | Prasiolales_X | Desmococcus | Desmococcus_endolithicus | NA | NA | 1 | NA | NA |
| Chlorophyta | Trebouxiophyceae | Watanabea-Clade | Watanabea-Clade_X | Chloroidium | Chloroidium_ellipsoideum | NA | NA | 1 | NA | NA |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Falcomonas | Falcomonas_sp. | NA | NA | 2 | NA | NA |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_rex | NA | NA | 1 | 1 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Hemiaulus | Hemiaulus_sinensis | NA | NA | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_minuscula | NA | NA | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_oestrupii | NA | NA | 1 | 1 | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Cylindrotheca | Cylindrotheca_closterium | NA | NA | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Navicula | Navicula_perminuta | NA | NA | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Nitzschia | Nitzschia_sp. | NA | NA | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pseudo-nitzschia | Pseudo-nitzschia_turgidula | NA | NA | 1 | NA | NA |
| Ochrophyta | Bolidophyceae | Parmales | Triparmaceae | Triparma | Triparma_laevis_f._inornata | NA | NA | 1 | NA | NA |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Saccharina | Saccharina_japonica | NA | NA | 1 | NA | NA |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Falcomonas | Falcomonas_daucoides | NA | NA | NA | 3 | 1 |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Hemiselmis | Hemiselmis_sp. | NA | NA | NA | 1 | NA |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_pouchetii | NA | NA | NA | 2 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Bacterosira | Bacterosira_sp. | NA | NA | NA | 1 | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Bacillaria | Bacillaria_paxillifer | NA | NA | NA | 1 | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Luka_AeN707 | Luka_AeN707-96_sp. | NA | NA | NA | 2 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pseudo-nitzschia | Pseudo-nitzschia_sp. | NA | NA | NA | 2 | 1 |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Florenciellales | Pseudochattonella | Pseudochattonella_verruculosa | NA | NA | NA | 1 | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Skeletonema | Skeletonema_sp. | NA | NA | NA | NA | 1 |
| Ochrophyta | Pelagophyceae | Pelagomonadales | Pelagomonadaceae | Pelagomonadaceae_clade_A | Pelagomonadaceae_clade_A_sp. | NA | NA | NA | NA | 2 |
| Ochrophyta | Pelagophyceae | Sarcinochrysidales | Sarcinochrysidaceae | Ankylochrysis | Ankylochrysis_sp. | NA | NA | NA | NA | 1 |
| Ochrophyta | Pelagophyceae | Sarcinochrysidales | Sarcinochrysidaceae | Sarcinochrysis | Sarcinochrysis_marina | NA | NA | NA | NA | 1 |
png
2
Comparison of species found in the different fractions for 18S filter
Compute table of number of samples for each species (rows) vs the three fractions (columns)
Only keep
- Genera that do not contain _X
We consider now ALL samples (will need to remove samples for which the > 20 um is missing)
long_species_fraction <- list()
long_class_fraction <- list()
for (one_sample_type in sample_type[c(1)]) {
long_one <- long_species_sample[[one_sample_type]] %>% filter(!str_detect(species,
"_X")) %>% group_by(fraction_name, division, class, order, family, genus,
species) %>% summarize(n_samples = n()) %>% pivot_wider(names_from = fraction_name,
values_from = n_samples) %>% ungroup()
long_species_fraction[[one_sample_type]] <- long_one
long_one <- long_class_sample[[one_sample_type]] %>% group_by(fraction_name,
division, class) %>% summarize(n_samples = n()) %>% pivot_wider(names_from = fraction_name,
values_from = n_samples) %>% ungroup()
long_class_fraction[[one_sample_type]] <- long_one
}
long_class_fraction_comparison <- long_class_fraction[[one_sample_type]]
long_species_fraction_comparison <- long_species_fraction[[one_sample_type]]
kable(long_class_fraction_comparison)| division | class | > 20 um | 0.2-3 um | 3-20 um |
|---|---|---|---|---|
| Chlorophyta | Chlorophyceae | 8 | 6 | 6 |
| Chlorophyta | Mamiellophyceae | 31 | 43 | 44 |
| Chlorophyta | Palmophyllophyceae | 5 | 26 | 25 |
| Chlorophyta | Pyramimonadophyceae | 29 | 39 | 44 |
| Chlorophyta | Trebouxiophyceae | 9 | 1 | 7 |
| Chlorophyta | Ulvophyceae | 29 | 27 | 29 |
| Cryptophyta | Cryptophyceae | 36 | 43 | 44 |
| Haptophyta | Prymnesiophyceae | 33 | 43 | 43 |
| Ochrophyta | Bacillariophyta | 36 | 43 | 44 |
| Ochrophyta | Bolidophyceae | 35 | 43 | 44 |
| Ochrophyta | Chrysophyceae | 29 | 42 | 42 |
| Ochrophyta | Dictyochophyceae | 28 | 43 | 44 |
| Ochrophyta | MOCH-1 | 1 | 3 | 4 |
| Ochrophyta | MOCH-2 | 15 | 24 | 36 |
| Ochrophyta | Pelagophyceae | 35 | 43 | 44 |
| Ochrophyta | Phaeophyceae | 34 | 34 | 43 |
| Ochrophyta | Xanthophyceae | 1 | NA | NA |
| Rhodophyta | Bangiophyceae | 2 | 1 | NA |
| Rhodophyta | Florideophyceae | 35 | 37 | 43 |
| division | class | order | family | genus | species | > 20 um | 0.2-3 um | 3-20 um |
|---|---|---|---|---|---|---|---|---|
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Chlamydomonas | Chlamydomonas_hedleyi | 1 | 1 | 1 |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Chlamydomonas | Chlamydomonas_kuwadae | 5 | 3 | 4 |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Chlamydomonas | Chlamydomonas_raudensis | 1 | NA | 1 |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Pleurastrum | Pleurastrum_sp. | 1 | 1 | 2 |
| Chlorophyta | Chlorophyceae | Sphaeropleales | Sphaeropleales_X | Radiococcus | Radiococcus_polycoccus | 1 | NA | NA |
| Chlorophyta | Mamiellophyceae | Dolichomastigales | Dolichomastigaceae | Dolichomastigaceae-B | Dolichomastigaceae-B_sp. | 15 | 26 | 27 |
| Chlorophyta | Mamiellophyceae | Mamiellales | Bathycoccaceae | Bathycoccus | Bathycoccus_prasinos | 14 | 41 | 41 |
| Chlorophyta | Mamiellophyceae | Mamiellales | Mamiellaceae | Micromonas | Micromonas_clade_B3 | 2 | 20 | 4 |
| Chlorophyta | Mamiellophyceae | Mamiellales | Mamiellaceae | Micromonas | Micromonas_polaris | 26 | 42 | 41 |
| Chlorophyta | Palmophyllophyceae | Prasinococcales | Prasinococcales-Clade-B | Prasinoderma | Prasinoderma_sp. | 5 | 26 | 25 |
| Chlorophyta | Pyramimonadophyceae | Pyramimonadales | Pyramimonadaceae | Pyramimonas | Pyramimonas_australis | 17 | 29 | 35 |
| Chlorophyta | Pyramimonadophyceae | Pyramimonadales | Pyramimonadaceae | Pyramimonas | Pyramimonas_gelidicola | 28 | 39 | 44 |
| Chlorophyta | Trebouxiophyceae | Chlorellales | Chlorellales_X | Chlorella | Chlorella_sorokiniana | 1 | NA | NA |
| Chlorophyta | Trebouxiophyceae | Prasiolales | Prasiolales_X | Koliella | Koliella_sempervirens | 1 | NA | NA |
| Chlorophyta | Trebouxiophyceae | Watanabea-Clade | Watanabea-Clade_X | Chloroidium | Chloroidium_saccharophila | 2 | 1 | 1 |
| Chlorophyta | Ulvophyceae | Ulotrichales | Ulotrichales_X | Chlorothrix | Chlorothrix_sp. | 16 | 5 | 8 |
| Chlorophyta | Ulvophyceae | Ulotrichales | Ulotrichales_X | Monostroma | Monostroma_grevillei | 12 | 11 | 14 |
| Chlorophyta | Ulvophyceae | Ulotrichales | Ulotrichales_X | Ulothrix | Ulothrix_zonata | 2 | 2 | 1 |
| Chlorophyta | Ulvophyceae | Ulvales-relatives | Ulvales-relatives_X | Acrochaete | Acrochaete_leptochaete | 5 | 8 | 13 |
| Chlorophyta | Ulvophyceae | Ulvales-relatives | Ulvales-relatives_X | Dilabifilum | Dilabifilum_sp. | 3 | 5 | 5 |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Geminigera | Geminigera_cryophila | 36 | 43 | 44 |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Hemiselmis | Hemiselmis_tepida | 2 | 7 | 7 |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_antarctica | 33 | 43 | 43 |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_sp. | 1 | NA | 1 |
| Haptophyta | Prymnesiophyceae | Prymnesiales | Chrysochromulinaceae | Chrysochromulina | Chrysochromulina_sp. | 6 | 23 | 31 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Asteroplanus | Asteroplanus_karianus | 35 | 39 | 43 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Grammonema | Grammonema_striatula | 9 | 2 | 8 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Licmophora | Licmophora_grandis | 17 | 1 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Pteroncola | Pteroncola_inane | 8 | 1 | 4 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Synedra | Synedra_hyperborea | 2 | 1 | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Synedropsis | Synedropsis_recta | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Tabularia | Tabularia_tabulata | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Thalassionema | Thalassionema_frauenfeldii | 2 | 1 | 2 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Thalassiothrix | Thalassiothrix_longissima | 4 | 2 | 3 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_danicus | 4 | 1 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_debilis_2 | 12 | 9 | 9 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_dichaeta | 3 | 3 | 2 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_neogracilis | 33 | 38 | 43 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_peruvianus | 2 | 3 | 3 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_rostratus | 10 | 3 | 7 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_socialis | 25 | 22 | 14 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Ditylum | Ditylum_sol | 2 | 1 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Eucampia | Eucampia_antarctica | 14 | 14 | 11 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Hemiaulus | Hemiaulus_sinensis | 3 | 3 | 6 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Minidiscus | Minidiscus_sp. | 36 | 42 | 44 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Minidiscus | Minidiscus_trioculatus | 1 | 2 | 12 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Odontella | Odontella_aurita | 2 | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Odontella | Odontella_mobiliensis | 4 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Porosira | Porosira_glacialis | 36 | 43 | 42 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Porosira | Porosira_pseudodelicatula | 4 | 2 | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Porosira | Porosira_pseudodenticulata | 2 | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Shionodiscus | Shionodiscus_ritscheri | 20 | 3 | 4 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_antarctica | 1 | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_minima | 35 | 41 | 44 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_oceanica | 1 | 1 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_rotula | 1 | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_sp. | 33 | 37 | 36 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_tumida | 12 | 1 | 2 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Actinocyclus | Actinocyclus_actinochilus | 23 | 5 | 4 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Actinocyclus | Actinocyclus_curvatulus | 5 | 2 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Asteromphalus | Asteromphalus_sp. | 2 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Corethron | Corethron_inerme | 36 | 42 | 43 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Coscinodiscus | Coscinodiscus_concinnus | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Coscinodiscus | Coscinodiscus_jonesianus | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Guinardia | Guinardia_solstherfothii | 1 | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Proboscia | Proboscia_alata | 19 | 12 | 23 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Proboscia | Proboscia_inermis | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Rhizosolenia | Rhizosolenia_fallax | 1 | 1 | 2 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Rhizosolenia | Rhizosolenia_imbricata_var_shrubsolei | 2 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Stellarima | Stellarima_microtrias | 10 | 4 | 2 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Achnanthes | Achnanthes_bongranii | 3 | NA | 2 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Amphora | Amphora_proteus | 18 | 3 | 3 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Cocconeis | Cocconeis_stauroneiformis | 1 | 1 | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Cymbella | Cymbella_gastroides | 10 | 7 | 21 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Cymbella | Cymbella_laevis | 3 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Cymbella | Cymbella_salina | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Dickieia | Dickieia_ulvacea | 4 | 1 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Encyonema | Encyonema_sp. | 12 | 4 | 8 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Fragilariopsis | Fragilariopsis_cylindrus | 36 | 43 | 44 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Fragilariopsis | Fragilariopsis_sublineata | 14 | 7 | 29 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Gyrosigma | Gyrosigma_limosum | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Haslea | Haslea_spicula | 11 | 12 | 14 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Navicula | Navicula_lanceolata | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Navicula | Navicula_phyllepta | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Navicula | Navicula_sp. | 10 | 7 | 9 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Naviculales | Naviculales_sp. | 4 | 11 | 15 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pinnularia | Pinnularia_microstauron | 1 | NA | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pleurosigma | Pleurosigma_intermedium | 3 | 1 | 2 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pseudo-nitzschia | Pseudo-nitzschia_seriata | 30 | 22 | 32 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pseudogomphonema | Pseudogomphonema_sp. | 12 | 6 | 7 |
| Ochrophyta | Bolidophyceae | Parmales | Parmales_env_3 | Parmales_env_3A | Parmales_env_3A_sp. | 5 | 28 | 12 |
| Ochrophyta | Bolidophyceae | Parmales | Parmales_env_3 | Parmales_env_3B | Parmales_env_3B_sp. | 29 | 42 | 42 |
| Ochrophyta | Bolidophyceae | Parmales | Triparmaceae | Triparma | Triparma_laevis_clade | 35 | 42 | 44 |
| Ochrophyta | Chrysophyceae | Chrysophyceae_X | Chrysophyceae_Clade-C | Pedospumella | Pedospumella_encystans | 1 | 1 | NA |
| Ochrophyta | Chrysophyceae | Chrysophyceae_X | Chrysophyceae_Clade-C | Spumella | Spumella_elongata | 2 | 13 | 7 |
| Ochrophyta | Chrysophyceae | Chrysophyceae_X | Chrysophyceae_Clade-F | Paraphysomonas | Paraphysomonas_butcheri | 1 | 4 | 3 |
| Ochrophyta | Chrysophyceae | Chrysophyceae_X | Chrysophyceae_Clade-F | Paraphysomonas | Paraphysomonas_imperforata | 3 | 8 | 4 |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Dictyochales | Dictyocha | Dictyocha_speculum | 2 | 5 | 4 |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Florenciellales | Florenciella | Florenciella_parvula | 13 | 38 | 41 |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Florenciellales | Pseudochattonella | Pseudochattonella_farcimen | 10 | 32 | 43 |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Florenciellales | Pseudochattonella | Pseudochattonella_sp. | 3 | 10 | 13 |
| Ochrophyta | Pelagophyceae | Pelagomonadales | Pelagomonadaceae | Pelagomonas | Pelagomonas_calceolata | 6 | 28 | 16 |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Desmarestia | Desmarestia_menziesii | 23 | 9 | 17 |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Desmarestia | Desmarestia_sp. | 20 | 16 | 23 |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Ectocarpus | Ectocarpus_siliculosus | 8 | 2 | 3 |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Phaeurus | Phaeurus_antarcticus | 28 | 10 | 21 |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Pylaiella | Pylaiella_littoralis | 4 | NA | 2 |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Saccharina | Saccharina_japonica | 1 | 2 | 6 |
| Ochrophyta | Xanthophyceae | Xanthophyceae_X | Xanthophyceae_XX | Botrydiopsis | Botrydiopsis_callosa | 1 | NA | NA |
| Rhodophyta | Bangiophyceae | Bangiales | Bangiaceae | Porphyra | Porphyra_plocamiestris | 1 | NA | NA |
| Rhodophyta | Bangiophyceae | Bangiales | Bangiaceae | Pyropia | Pyropia_sp. | 1 | NA | NA |
| Rhodophyta | Florideophyceae | Ceramiales | Callithamniaceae | Diapse | Diapse_ptilota | 2 | 1 | NA |
| Rhodophyta | Florideophyceae | Ceramiales | Delesseriaceae | Phycodrys | Phycodrys_rubens | 7 | 2 | 7 |
| Rhodophyta | Florideophyceae | Ceramiales | Sarcomeniaceae | Platysiphonia | Platysiphonia_victoriae | 1 | 1 | NA |
| Rhodophyta | Florideophyceae | Colaconematales | Colaconematales_X | Palmaria | Palmaria_palmata | 14 | 18 | 18 |
| Rhodophyta | Florideophyceae | Corallinales | Corallinales_X | Hapalidiaceae | Hapalidiaceae_sp. | 7 | 2 | 1 |
| Rhodophyta | Florideophyceae | Corallinales | Corallinales_X | Mesophyllum | Mesophyllum_sp. | 32 | 22 | 36 |
| Rhodophyta | Florideophyceae | Corallinales | Corallinales_X | Synarthrophyton | Synarthrophyton_patena | 11 | 2 | 1 |
| Rhodophyta | Florideophyceae | Gigartinales | Gigartinales_X | Chondrus | Chondrus_nipponicus | 34 | 22 | 41 |
| Rhodophyta | Florideophyceae | Gigartinales | Gigartinales_X | Schottera | Schottera_nicaeensis | 1 | NA | NA |
| Rhodophyta | Florideophyceae | Gigartinales | Gigartinales_X | Stenogramme | Stenogramme_interrupta | 4 | NA | 2 |
| Rhodophyta | Florideophyceae | Gracilariales | Gracilariales_X | Curdiea | Curdiea_codiodes | 14 | 4 | 9 |
| Rhodophyta | Florideophyceae | Plocamiales | Plocamiales_X | Trematocarpus | Trematocarpus_fragilis | 10 | 1 | 1 |
| Chlorophyta | Chlorophyceae | Chaetopeltidales | Chaetopeltidaceae | Planophila | Planophila_sp. | NA | 1 | NA |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Chlamydomonas | Chlamydomonas_acidophila | NA | 1 | NA |
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Haematococcus | Haematococcus_zimbabwiensis | NA | 1 | NA |
| Chlorophyta | Mamiellophyceae | Mamiellales | Mamiellaceae | Mantoniella | Mantoniella_squamata | NA | 4 | 1 |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Plagioselmis | Plagioselmis_nannoplanctica | NA | 1 | 1 |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Teleaulax | Teleaulax_sp. | NA | 1 | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_gelidus | NA | 3 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Guinardia | Guinardia_delicatula | NA | 1 | 2 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Nitzschia | Nitzschia_dissipata | NA | 1 | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pauliella | Pauliella_toeniata | NA | 1 | 1 |
| Ochrophyta | Bolidophyceae | Parmales | Triparmaceae | Triparma | Triparma_sp. | NA | 6 | 5 |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Pedinellales | Pseudopedinella | Pseudopedinella_sp. | NA | 1 | 2 |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Pedinellales | Pteridomonas | Pteridomonas_danica | NA | 1 | NA |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Desmarestia | Desmarestia_anceps | NA | 1 | NA |
| Rhodophyta | Bangiophyceae | Bangiales | Bangiaceae | Pyropia | Pyropia_dentata | NA | 1 | NA |
| Rhodophyta | Florideophyceae | Ceramiales | Dasyaceae | Dasya | Dasya_collabens | NA | 1 | NA |
| Rhodophyta | Florideophyceae | Ceramiales | Delesseriaceae | Hemineura | Hemineura_frondosa | NA | 1 | NA |
| Rhodophyta | Florideophyceae | Ceramiales | Rhodomelaceae | Rhodomela | Rhodomela_confervoides | NA | 1 | 1 |
| Rhodophyta | Florideophyceae | Gigartinales | Gigartinales_X | Kallymenia | Kallymenia_tasmanica | NA | 1 | 2 |
| Rhodophyta | Florideophyceae | Halymeniales | Halymeniales_X | Pachymenia | Pachymenia_orbicularis | NA | 1 | NA |
| Chlorophyta | Trebouxiophyceae | Prasiolales | Prasiolales_X | Desmococcus | Desmococcus_endolithicus | NA | NA | 1 |
| Chlorophyta | Trebouxiophyceae | Prasiolales | Prasiolales_X | Prasiola | Prasiola_crispa | NA | NA | 3 |
| Chlorophyta | Trebouxiophyceae | Watanabea-Clade | Watanabea-Clade_X | Chloroidium | Chloroidium_ellipsoideum | NA | NA | 1 |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Falcomonas | Falcomonas_sp. | NA | NA | 2 |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_rex | NA | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Porosira | Porosira_sp. | NA | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_minuscula | NA | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_oestrupii | NA | NA | 2 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Cylindrotheca | Cylindrotheca_closterium | NA | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Navicula | Navicula_perminuta | NA | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Nitzschia | Nitzschia_sp. | NA | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pseudo-nitzschia | Pseudo-nitzschia_turgidula | NA | NA | 1 |
| Ochrophyta | Bolidophyceae | Parmales | Triparmaceae | Triparma | Triparma_laevis_f._inornata | NA | NA | 1 |
| Rhodophyta | Florideophyceae | Gigartinales | Gigartinales_X | Delisea | Delisea_pulchra | NA | NA | 1 |
png
2
Comparison of species found in the different fractions for 18S sort
Compute table of number of samples for each species (rows) vs the three fractions (columns)
Only keep
- Genera that do not contain _X
We consider now ALL samples (will need to remove samples for which the > 20 um is missing)
long_species_fraction <- list()
long_class_fraction <- list()
for (one_sample_type in sample_type[c(3)]) {
long_one <- long_species_sample[[one_sample_type]] %>% filter(!str_detect(species,
"_X")) %>% group_by(fraction_name, division, class, order, family, genus,
species) %>% summarize(n_samples = n()) %>% pivot_wider(names_from = fraction_name,
values_from = n_samples) %>% ungroup()
long_species_fraction[[one_sample_type]] <- long_one
long_one <- long_class_sample[[one_sample_type]] %>% group_by(fraction_name,
division, class) %>% summarize(n_samples = n()) %>% pivot_wider(names_from = fraction_name,
values_from = n_samples) %>% ungroup()
long_class_fraction[[one_sample_type]] <- long_one
}
long_class_fraction_comparison <- long_class_fraction[[one_sample_type]]
long_species_fraction_comparison <- long_species_fraction[[one_sample_type]]
kable(long_class_fraction_comparison)| division | class | nano | pico |
|---|---|---|---|
| Chlorophyta | Chlorophyceae | 1 | NA |
| Chlorophyta | Mamiellophyceae | 18 | 22 |
| Chlorophyta | Palmophyllophyceae | 1 | 1 |
| Chlorophyta | Pyramimonadophyceae | 19 | 4 |
| Chlorophyta | Ulvophyceae | 2 | 2 |
| Cryptophyta | Cryptophyceae | 22 | 9 |
| Haptophyta | Prymnesiophyceae | 22 | 22 |
| Ochrophyta | Bacillariophyta | 22 | 22 |
| Ochrophyta | Bolidophyceae | 21 | 20 |
| Ochrophyta | Chrysophyceae | 2 | 2 |
| Ochrophyta | Dictyochophyceae | 15 | 1 |
| Ochrophyta | MOCH-2 | 11 | NA |
| Ochrophyta | Pelagophyceae | 22 | 22 |
| Ochrophyta | Phaeophyceae | 10 | NA |
| division | class | order | family | genus | species | nano | pico |
|---|---|---|---|---|---|---|---|
| Chlorophyta | Chlorophyceae | Chlamydomonadales | Chlamydomonadales_X | Chlamydomonas | Chlamydomonas_raudensis | 1 | NA |
| Chlorophyta | Mamiellophyceae | Dolichomastigales | Dolichomastigaceae | Dolichomastigaceae-B | Dolichomastigaceae-B_sp. | 1 | 5 |
| Chlorophyta | Mamiellophyceae | Mamiellales | Bathycoccaceae | Bathycoccus | Bathycoccus_prasinos | 1 | 22 |
| Chlorophyta | Mamiellophyceae | Mamiellales | Mamiellaceae | Mantoniella | Mantoniella_squamata | 1 | NA |
| Chlorophyta | Mamiellophyceae | Mamiellales | Mamiellaceae | Micromonas | Micromonas_polaris | 17 | 22 |
| Chlorophyta | Palmophyllophyceae | Prasinococcales | Prasinococcales-Clade-B | Prasinoderma | Prasinoderma_sp. | 1 | 1 |
| Chlorophyta | Pyramimonadophyceae | Pyramimonadales | Pyramimonadaceae | Pyramimonas | Pyramimonas_australis | 10 | 2 |
| Chlorophyta | Pyramimonadophyceae | Pyramimonadales | Pyramimonadaceae | Pyramimonas | Pyramimonas_gelidicola | 19 | 4 |
| Chlorophyta | Ulvophyceae | Ulotrichales | Ulotrichales_X | Monostroma | Monostroma_grevillei | 2 | 1 |
| Chlorophyta | Ulvophyceae | Ulotrichales | Ulotrichales_X | Ulothrix | Ulothrix_zonata | 1 | NA |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Falcomonas | Falcomonas_daucoides | 3 | 1 |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Geminigera | Geminigera_cryophila | 22 | 8 |
| Cryptophyta | Cryptophyceae | Cryptomonadales | Cryptomonadales_X | Hemiselmis | Hemiselmis_sp. | 1 | NA |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_antarctica | 22 | 20 |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_pouchetii | 2 | 1 |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_rex | 1 | 1 |
| Haptophyta | Prymnesiophyceae | Phaeocystales | Phaeocystaceae | Phaeocystis | Phaeocystis_sp. | 12 | 2 |
| Haptophyta | Prymnesiophyceae | Prymnesiales | Chrysochromulinaceae | Chrysochromulina | Chrysochromulina_sp. | 22 | 5 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Araphid-pennate | Asteroplanus | Asteroplanus_karianus | 20 | 3 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Bacterosira | Bacterosira_sp. | 1 | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_neogracilis | 16 | 19 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Chaetoceros | Chaetoceros_socialis | 3 | 3 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Minidiscus | Minidiscus_sp. | 22 | 10 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Porosira | Porosira_glacialis | 2 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_minima | 21 | 11 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_oestrupii | 1 | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Thalassiosira | Thalassiosira_sp. | 14 | 2 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Bacillaria | Bacillaria_paxillifer | 1 | NA |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Fragilariopsis | Fragilariopsis_cylindrus | 22 | 20 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Fragilariopsis | Fragilariopsis_sublineata | 6 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Luka_AeN707 | Luka_AeN707-96_sp. | 2 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Naviculales | Naviculales_sp. | 4 | 2 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pseudo-nitzschia | Pseudo-nitzschia_seriata | 6 | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Raphid-pennate | Pseudo-nitzschia | Pseudo-nitzschia_sp. | 2 | 1 |
| Ochrophyta | Bolidophyceae | Parmales | Parmales_env_3 | Parmales_env_3B | Parmales_env_3B_sp. | 21 | 15 |
| Ochrophyta | Bolidophyceae | Parmales | Triparmaceae | Triparma | Triparma_laevis_clade | 11 | 17 |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Florenciellales | Florenciella | Florenciella_parvula | 9 | NA |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Florenciellales | Pseudochattonella | Pseudochattonella_farcimen | 9 | NA |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Florenciellales | Pseudochattonella | Pseudochattonella_sp. | 2 | NA |
| Ochrophyta | Dictyochophyceae | Dictyochophyceae_X | Florenciellales | Pseudochattonella | Pseudochattonella_verruculosa | 1 | NA |
| Ochrophyta | Pelagophyceae | Pelagomonadales | Pelagomonadaceae | Pelagomonas | Pelagomonas_calceolata | 3 | 18 |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Desmarestia | Desmarestia_menziesii | 3 | NA |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Desmarestia | Desmarestia_sp. | 3 | NA |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Ectocarpus | Ectocarpus_siliculosus | 2 | NA |
| Ochrophyta | Phaeophyceae | Phaeophyceae_X | Phaeophyceae_XX | Phaeurus | Phaeurus_antarcticus | 7 | NA |
| Chlorophyta | Mamiellophyceae | Mamiellales | Mamiellaceae | Micromonas | Micromonas_clade_B3 | NA | 21 |
| Chlorophyta | Ulvophyceae | Ulvales-relatives | Ulvales-relatives_X | Acrochaete | Acrochaete_leptochaete | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Polar-centric-Mediophyceae | Skeletonema | Skeletonema_sp. | NA | 1 |
| Ochrophyta | Bacillariophyta | Bacillariophyta_X | Radial-centric-basal-Coscinodiscophyceae | Corethron | Corethron_inerme | NA | 1 |
| Ochrophyta | Pelagophyceae | Pelagomonadales | Pelagomonadaceae | Pelagomonadaceae_clade_A | Pelagomonadaceae_clade_A_sp. | NA | 2 |
| Ochrophyta | Pelagophyceae | Sarcinochrysidales | Sarcinochrysidaceae | Ankylochrysis | Ankylochrysis_sp. | NA | 1 |
| Ochrophyta | Pelagophyceae | Sarcinochrysidales | Sarcinochrysidaceae | Sarcinochrysis | Sarcinochrysis_marina | NA | 1 |
png
2
Figures
Figure 2 - Treemap
treemap_18S_filt <- cowplot::plot_grid(treemap_class[["18S filter 0.2 um surface"]]$gg,
treemap_class[["18S filter 3 um surface"]]$gg, treemap_class[["18S filter 20 um surface"]]$gg,
ncol = 1)
treemap_18S_sort <- cowplot::plot_grid(treemap_class[["18S sort pico surface"]]$gg,
treemap_class[["18S sort nano surface"]]$gg, treemap_class[["fill in"]]$gg,
ncol = 1)
treemap_16S_filt <- cowplot::plot_grid(treemap_class[["16S plastid 0.2 um surface"]]$gg,
treemap_class[["16S plastid 3 um surface"]]$gg, treemap_class[["16S plastid 20 um surface"]]$gg,
ncol = 1)
fig_2 <- cowplot::plot_grid(bargraph_species[["18S filter surface"]]$gg, treemap_18S_filt,
bargraph_species[["18S sort surface"]]$gg, treemap_18S_sort, bargraph_species[["16S plastid surface"]]$gg,
treemap_16S_filt, ncol = 2, rel_widths = c(2, 1))
fig_2Figure Supp - Treemap at genus level
treemap_18S_filt <- cowplot::plot_grid(treemap_genus[["18S filter 0.2 um surface"]]$gg,
treemap_genus[["18S filter 3 um surface"]]$gg, treemap_genus[["18S filter 20 um surface"]]$gg,
ncol = 1)
treemap_18S_sort <- cowplot::plot_grid(treemap_genus[["18S sort pico surface"]]$gg,
treemap_genus[["18S sort nano surface"]]$gg, treemap_genus[["fill in"]]$gg,
ncol = 1)
treemap_16S_filt <- cowplot::plot_grid(treemap_genus[["16S plastid 0.2 um surface"]]$gg,
treemap_genus[["16S plastid 3 um surface"]]$gg, treemap_genus[["16S plastid 20 um surface"]]$gg,
ncol = 1)
fig_supp_treemap <- cowplot::plot_grid(treemap_18S_filt, treemap_18S_sort, treemap_16S_filt,
labels = c("A", "B", "C"), label_x = 0.9, ncol = 3)
fig_supp_treemapFigure 3 - NMDS 18S filter
fig_3 <- cowplot::plot_grid(nmds_sample[["18S filter surface"]]$gg_samples,
nmds_sample[["18S filter surface"]]$gg_taxa, labels = c("A", "B"), label_x = 0.9,
nrow = 2, rel_widths = c(1, 1))
fig_3Figure sup - NMDS 16S plastid
fig_3 <- cowplot::plot_grid(nmds_sample[["16S plastid surface"]]$gg_samples,
nmds_sample[["16S plastid surface"]]$gg_taxa, labels = c("A", "B"), label_x = 0.9,
nrow = 2, rel_widths = c(1, 1))
fig_3Figure 4 - Heatmap 18S filter
fig_4 <- cowplot::plot_grid(heatmap_class_selected[["18S filter 0.2 um surface"]],
heatmap_class_selected[["18S filter 3 um surface"]], heatmap_class_selected[["18S filter 20 um surface"]],
heatmap_species_selected[["18S filter 0.2 um surface"]], heatmap_species_selected[["18S filter 3 um surface"]],
heatmap_species_selected[["18S filter 20 um surface"]], labels = c("A",
"B", "C", "D", "E", "F"), label_x = 0.9, nrow = 2, ncol = 3)
fig_4Figure sup - Heatmap 18S sort
fig_heatmap_sort <- cowplot::plot_grid(heatmap_class_selected[["18S sort pico surface"]],
heatmap_class_selected[["18S sort nano surface"]], heatmap_species_selected[["18S sort pico surface"]],
heatmap_species_selected[["18S sort nano surface"]], labels = c("A", "B",
"C", "D"), label_x = 0.9, nrow = 2, ncol = 2)
fig_heatmap_sortFigure 6 - Vertical Profile
legend <- cowplot::get_legend( bargraph_sample[["18S filter 0.2 um profile"]] +
# create some space to the left of the legend
theme(legend.box.margin = margin(0, 0, 0, 20))
)
fig_6 <- cowplot::plot_grid(bargraph_sample[["18S filter 0.2 um profile"]]+ theme(legend.position="none"),legend,
bargraph_sample[["18S filter 3 um profile"]] + theme(legend.position="none"),NULL,
bargraph_sample[["18S filter 20 um profile"]]+ theme(legend.position="none"),NULL,
labels = c("" ,"A","", "B","", "C"), label_x = 0.9,
nrow=3, ncol=2, rel_widths = c(3, 1.7))
fig_6Figure X - Upset R
- Problem is that upset does not provide a ggplot object. So need to save as png
- Use ggplotify library : https://cran.r-project.org/web/packages/ggplotify/vignettes/ggplotify.html
- But it heats a bit the plots
p1 <- cowplot::ggdraw() + cowplot::draw_image("fig/upset_method.png")
p2 <- cowplot::ggdraw() + cowplot::draw_image("fig/upset_filter.png")
p3 <- cowplot::ggdraw() + cowplot::draw_image("fig/upset_sort.png")
fig_X_upset <- cowplot::plot_grid(p1, p2, p3, labels = c("A", "B", "C"), label_x = 0.9,
nrow = 3, ncol = 1)
fig_X_upset